class_docstr: ReID of SDE methods
  
  Args:
      pred_config (object): config of model, defined by `Config(model_dir)`
      model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
      device (str): Choose the device you want to run, it can be: CPU/GPU/XPU/NPU, default is CPU
      run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
      batch_size (int): size of per batch in inference, default 50 means at most
          50 sub images can be made a batch and send into ReID model
      trt_min_shape (int): min shape for dynamic shape in trt
      trt_max_shape (int): max shape for dynamic shape in trt
      trt_opt_shape (int): opt shape for dynamic shape in trt
      trt_calib_mode (bool): If the model is produced by TRT offline quantitative
          calibration, trt_calib_mode need to set True
      cpu_threads (int): cpu threads
      enable_mkldnn (bool): whether to open MKLDNN
class_docstr_tok: ["ReID","of","SDE","methods","Args",":","pred_config","(","object",")",":","config","of","model",",","defined","by","`","Config","(","model_dir",")","`","model_dir","(","str",")",":","root","path","of","model.pdiparams",",","model.pdmodel","and","infer_cfg.yml","device","(","str",")",":","Choose","the","device","you","want","to","run",",","it","can","be",":","CPU\/GPU\/XPU\/NPU",",","default","is","CPU","run_mode","(","str",")",":","mode","of","running","(","paddle\/trt_fp32\/trt_fp16",")","batch_size","(","int",")",":","size","of","per","batch","in","inference",",","default","50","means","at","most","50","sub","images","can","be","made","a","batch","and","send","into","ReID","model","trt_min_shape","(","int",")",":","min","shape","for","dynamic","shape","in","trt","trt_max_shape","(","int",")",":","max","shape","for","dynamic","shape","in","trt","trt_opt_shape","(","int",")",":","opt","shape","for","dynamic","shape","in","trt","trt_calib_mode","(","bool",")",":","If","the","model","is","produced","by","TRT","offline","quantitative","calibration",",","trt_calib_mode","need","to","set","True","cpu_threads","(","int",")",":","cpu","threads","enable_mkldnn","(","bool",")",":","whether","to","open","MKLDNN"]
code: def set_config(self, model_dir):
      return PredictConfig(model_dir)
  
file_path: PaddleDetection/deploy/pipeline/pphuman/reid.py
imports: import os
  import sys
  import cv2
  import numpy
  from python.infer import PredictConfig
  from pptracking.python.det_infer import load_predictor
  from python.utils import Timer
label_desc: Use image node_id 3 for calling the ReID obj's underlying member method code with example usage: obj.set_config(model_dir) and returns: PredictConfig